Random search with adaptive boundaries algorithm for obtaining better initial solutions

Abstract

Conventional random search techniques take a lot of time to reach optimum-like solutions. Thus, random search techniques with advanced competencies play an essential role in algorithms. In this study, we develop an algorithm that provides an adaptive initial solution, to some extent reducing the diversity of randomness in the initialization of the algorithms for continuous unconstrained/bounded nonlinear optimization problems. The algorithm meets this expectation by narrowing search space adaptively without trapping into local optimums. It also escapes from eliminating accidentally global optimum in multi-modal problems. For this reason, we configure the proposed algorithm on the principle of updating given upper-lower boundaries dynamically. It is worth mentioning that this procedure does not add an additional burden to existing solution methods; on the contrary, it contributes to problem-solving in terms of time and efficiency. To show its performance, we have incorporated with most frequently used unconstrained/bounded benchmarks and compared them with the solutions in the literature. In conclusion, the proposed algorithm converges solutions quickly and is applicable for later usage in further studies.